Podcasts about Product management

Risa Eldridge, AVP of Product Management & Integrations at CallMiner, spoke with Doug Green, Publisher of Technology Reseller News, during the Enterprise Connect conference about how conversation intelligence is helping organizations better understand customer interactions and improve operational performance. Eldridge explained that enterprises are increasingly looking to AI-driven analytics to extract meaningful insights from the vast volume of conversations taking place across contact centers and customer engagement channels. By analyzing voice and digital interactions, organizations can identify patterns, detect compliance risks, and uncover opportunities to improve both customer satisfaction and agent performance. “Conversation intelligence allows organizations to turn everyday interactions into actionable insights that drive better outcomes for both customers and employees,” she said. CallMiner's platform applies advanced analytics and artificial intelligence to help companies monitor and evaluate interactions at scale. These capabilities enable organizations to identify emerging trends, improve quality management programs, and provide more targeted coaching and training for agents. The conversation also highlighted how the rapid adoption of AI across the contact center is creating new expectations for visibility and data-driven decision making. As organizations adopt automation and AI-assisted workflows, understanding what is happening inside customer conversations becomes increasingly critical. As Enterprise Connect continues to highlight the role of AI in enterprise communications and customer experience, CallMiner's approach to conversation intelligence demonstrates how organizations can leverage communications data to improve performance, compliance, and customer engagement. Learn more about CallMiner: https://callminer.com/

How many engineers does it take to run the ecommerce site for a retail company that does over a billion dollars in revenue per year? Well, if you're Rainbow Shops, the answer is just 2. Most ecommerce teams assume scale requires more engineers, more tools, more complexity. Chief Digital Officer David Cost has built something many people in ecommerce would say isn't possible — a lean, fast-moving digital operation that runs on vendor partnerships instead of a massive internal team. Two engineers, hundreds of programmers' worth of output, and none of the overhead that comes with scaling the traditional way. In this episode, David shares: A detailed, under-the-hood look at the specific vendors they use to stay so lean His playbook for using strategic partnerships with vendors as an external dev team How being a testbed for new tech gives them a competitive edge And why their choice of ecommerce platform was vital in enabling Rainbow's digital strategy Links David's LinkedIn: https://www.linkedin.com/in/davidcost/ Rainbow Shops: https://www.linkedin.com/company/rainbow-apparel-co/ Chapters 00:00 Introduction 00:42 David's product journey 02:36 How Rainbow runs with only two engineers 03:07 Rainbow's decision to migrate from  Salesforce Commerce Cloud to Shopify 07:14 How Rainbow uses AI to support a lean team 11:34 Rainbow's partnership with Lica for AI-generated product images 17:25 The future of personalization in ecommerce 23:01 Shop Pay and Rainbow's checkout features 26:15 Conclusion Resoures Lica: https://lica.world/ Fuego: https://fuego.io/ Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: David Cost.

Susy Liem, AVP of Product Management for Conferencing at Shure, spoke with Doug Green, Publisher of Technology Reseller News, during the Enterprise Connect conference about how organizations are rethinking conference room audio to better support hybrid collaboration. Liem explained that as enterprises continue adapting to hybrid work environments, the quality of audio in meeting spaces has become increasingly critical. Clear, reliable audio ensures that both in-room participants and remote attendees can fully engage in meetings without technical barriers. “Audio is often the most important part of the meeting experience—if people can't hear clearly, the entire collaboration breaks down,” she said. Shure has focused on developing conferencing solutions that simplify deployment while delivering professional-grade audio performance. The company's microphone and conferencing systems are designed to integrate seamlessly with modern collaboration platforms while supporting a wide range of room sizes and meeting configurations. Liem noted that organizations are moving beyond basic conferencing setups toward more advanced systems that automatically manage audio pickup, noise reduction, and speaker clarity. These capabilities help create a consistent experience for participants regardless of where they are located. As enterprise communications leaders gathered at Enterprise Connect to explore the future of collaboration technology, Shure highlighted the growing importance of high-quality audio infrastructure in enabling effective hybrid meetings and improving overall workplace communication. Learn more about Shure: https://www.shure.com/en-US

Greetings from SXSW, where I'm learning, recording, and eating... You'll hear all about it soon... For now, enjoy this short, sweet, and geeky bonus episode.Have you seen that weird graph about all the jobs that AI is going to kill? It looks like an ink blot or a Rorschach test... It's from an Anthropic report and it's really making the rounds. If you follow tech stuff on social media you've probably seen it. The report is interested, but I'm convinced people are only sharing it because the graph looks cool and people will think they're smart if they share this inscrutable data visualization... Anyway, here's a very short excerpt of my upcoming interview with Paul Ford (@ftrain), one of my favorite tech writers and the founder of Aboard. He and I took a break from talking AI and such to geek out on this data visualization and why it's so bad, plus I told him about how I used AI to make my own version of a radar graph (about how many, and which kinds of, tacos I will and could theoretically eat in Austin). ---Subscribe to the Future Around & Find Out newsletter!

Have you ever contacted customer support with a simple request, only to find yourself trapped in a loop of scripted chatbot responses that never actually solve the problem? It's an experience many of us know all too well.  AI has made customer service more conversational over the last few years, yet there is still a gap between answering a question and actually resolving an issue. That gap is exactly where today's conversation begins. In this episode of Tech Talks Daily, I spoke with Mike Szilagyi, SVP and General Manager of Product Management at Genesys Cloud, about a new chapter in AI-powered customer experience. Genesys has announced what it describes as the industry's first agentic virtual agent built on Large Action Models, or LAMs. While Large Language Models have dominated the conversation around AI for the past few years, they have largely focused on generating responses, retrieving knowledge, or answering questions. What they have struggled with is execution. Mike explained how Large Action Models take the next step. Rather than simply telling a customer how to solve a problem, these systems can plan and execute the steps needed to complete a task. Imagine contacting an airline after a sudden flight cancellation.  Instead of navigating multiple menus or repeating information to a human agent, an agentic virtual assistant could understand your situation, check alternative flights, apply airline policies, and complete the rebooking process across several systems. In other words, the AI moves from conversation to action. We also explored how Genesys approached the design of this technology with enterprise governance in mind. From explainable decision paths and audit logs to guardrails that ensure every automated action can be traced and understood, the goal is to make autonomous AI trustworthy inside complex organizations. Mike also shared insights into Genesys' partnership with Scaled Cognition and how integrating specialized models helps deliver reliable execution in real-world customer service environments. Perhaps most interesting was our discussion about the human role in this evolving contact center landscape. As automation begins to handle routine and multi-step workflows, human agents are free to focus on situations that require empathy, judgment, and expertise. That shift raises interesting questions about how organizations design customer experiences in the years ahead. So how will customers respond when virtual agents move beyond answering questions and begin resolving problems on their behalf? And once one brand delivers that experience, will it quickly become the expectation? Useful Links Connect with Mike Szilagyi Learn more about Genesys Genesys Agentic Virtual Agent Powered by LAMs for Enterprise CX Follow on LinkedIn

Today on the Salesforce Admins Podcast, we talk to Cheryl Feldman, Senior Director of Product Management at Salesforce. Join us as we chat about how Agentforce is reshaping the Setup experience. You should subscribe for the full episode, but here are a few takeaways from our conversation with Cheryl Feldman. From 1,300 pages to one […] The post The Future of Salesforce Setup Is Agent-Driven appeared first on Salesforce Admins.

Gautam Vasudev, SVP of Product Management for Agentforce Service at Salesforce, spoke with Doug Green, Publisher of Technology Reseller News, during the Enterprise Connect conference about Salesforce's vision for agentic AI and its role in transforming customer service operations. Vasudev explained that Agentforce Service represents Salesforce's next step in bringing autonomous AI agents into the contact center and customer experience environment. These AI-powered agents are designed to handle routine service requests, assist human agents in real time, and orchestrate workflows across enterprise systems. “Agentic AI allows organizations to move beyond simple automation and into systems that can actually reason through customer issues and take action,” Vasudev said. The goal is to improve both efficiency and customer satisfaction by enabling AI agents to resolve common service requests quickly while allowing human agents to focus on more complex interactions. By integrating AI directly into the Salesforce platform, Agentforce Service can connect customer data, workflows, and communications across the enterprise. Vasudev noted that enterprises are increasingly looking for AI solutions that go beyond chatbots and scripted automation. Agentic AI systems are designed to understand context, interact with multiple systems, and assist human workers in delivering more personalized customer experiences. As industry leaders gathered at Enterprise Connect to explore the future of enterprise communications and AI-powered collaboration, Salesforce highlighted how agentic AI platforms like Agentforce Service could redefine how organizations manage customer engagement and service delivery. Learn more about Salesforce: https://www.salesforce.com/

Today in Lighting is sponsored by BEGA, now bringing smart lighting control to projects of any scale. Learn more at https://utm.io/ulQzv. Highlights include: Signify Press Conference at Light + Building Highlights Connected Lighting Opportunity WAC Architectural Debuts at Light + Building 2026 Director, Product Management and Marketing with Leviton

In this episode of Denatured, you'll listen to Oxana Iliach, senior director of regulatory strategy at Certara and Vera Pomerantseva, director of product management for risk-based quality management at eClinical Solutions. We speak about how the FDA's latest decision to have one, rather than two pivotal studies, for new drug applications raises the bar for data collection and risk-based management.Host⁠Jennifer C. Smith-Parker⁠, Director of Insights, BioSpaceGuestsOxana Iliach, Senior Director of Regulatory Strategy, CertaraVera Pomerantseva, Director of Product Management for RBQM, eClinical SolutionsDisclaimer: The views expressed in this discussion by guests are their own and do not represent those of their organizations.

In this episode, Carlos Gonzalez de Villaumbrosia, CEO at Product School, sits down with Robby Stein, VP of Product at Google Search. Google Search serves billions of users and holds over 90% of the global market share, acting as the engine behind Alphabet's $400 billion in annual revenue.Robby is steering the most significant shift in Search history: the transition to AI. He oversees a massive portfolio, including the new AI Mode, which has already scaled to 75 million daily active users. Drawing from his time building Stories and Reels at Instagram, Robby breaks down how to build zero-to-one products inside a tech giant and why you need high conviction to push through early bad data.What you'll learn:Building Like a Startup: How to maintain crazy speed and focus on zero-to-one initiatives while protecting a massive core business.True Product-Market Fit: Why flat or J-curve retention in early cohorts is the only reliable indicator that a product is actually working.The Value of Colossal Disasters: The untold story of how early failures with Instagram Reels and Close Friends were necessary steps to global success.Agentic Search: How Google is moving beyond providing links to executing complex, multi-step tasks by deeply understanding personal context.Key takeaways:Start Small to Win Big: Even at Google's scale, massive AI products begin with just 500 trusted testers and a focus on solving specific user complaints.Look for the Golf Shot: When building AI, look for that rare moment when the whole system works perfectly to build the conviction needed to keep iterating.Leaders Must Co-Create: To move fast in a large org, leaders shouldn't just approve from the top; they need to form working groups and operate in the details.Credits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Robby SteinSocial Links: Follow our Podcast on Tik Tok here Follow Product School on LinkedIn here Join Product School's free events here Find out more about Product School here

WATCH THE VIDEO VERSION! https://youtu.be/qrcLV3tyCLo  For this video podcast, host Bill Calkins is joined by three representatives from PanAmerican Seed who each bring specific expertise to the table, having worked with new crops for years to help bring them to market with solid foundations and supported by all the information you, as growers, need to be successful. Lisa Lacy is Director of Product Management for PanAmerican, Robin Ruether is Senior Product Manager and Sonali Padhye is PanAm's Senior Global Technical Manager and together they will cover five key 2027 introductions from the global seed breeding company. Here's the list of crops and you'll see that they cover a range of genera, uses and market positions. There are landscape performers, combo components, Fleuroselect Gold Medal and All-America Selections winners and seed forms of traditionally vegetative varieties—truly something for everyone. Find all product information at https://www.panamseed.com/ . African Marigold Lanna Lace Bacopa Galactic Mist White Sunflower Always Sunny Gold Heuchera Heucherette Pink & Red Sedum Spectacular

Stop building products nobody wants by discovering the psychological bias killing your strategy!Today, Product Manager Brian Orlando and Enterprise Business Agility Coach Om Patel are taking on the "IKEA Effect." The IKEA effect is the cognitive bias that causes product managers and leaders to overvalue the things they build - simply because they built them! That's right, today we're tackling a bias that's led to so much wasted budget, we're going to end up needing Congressional oversight... not to mentioned the ignored research and "survivorship bias" of trying to be the next Steve Jobs.Listen or watch as we discuss and review:- The Scientific research behind the IKEA Effect (people value items 63% higher because they've built those items)- Why teams ignore expert research and undervalue insights THEY DID NOT SUFFER to obtain- How traditional review committees designed to kill bad ideas often stifle innovation (and what to do better)- Balancing the need for intrinsic motivation (Self-Determination Theory) with the necessity of governance- The "Kill or Nurture" Framework: A new 2x2 decision matrix to evaluate projects based on evidence vs. passionWe also share personal war stories on the product-related IKEA effect, bemoan the struggles of gaining funding for evidence-based ideas, and maybe even distinguish between a wobbly chair and a throne. Tune in if you're interested in ways to stop falling in love with your own bad ideas!#ProductManagement #Agile #ProductStrategyNorton, Mochon, & Ariely (2012) IKEA Effect Study, The IKEA Effect (Harvard Business Review), Self-Determination Theory (Ryan & Deci), Gartner Research, Deming's theories on intrinsic motivationLINKSYouTube: https://www.youtube.com/@arguingagileSpotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596INTRO MUSICToronto Is My BeatBy Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

So why is one of the world's leading AI researchers teaching AI to understand pain and suffering? Well, Daniel Hulme says that if we build an empathetic AI, perhaps even a conscious one, then we'll be safer. His hypothesis is that a "zombie" AI will eat our brains, but an empathetic AI would stay aligned with us. So he's building this "antivirus" (with AI, of course) and he's very aware that this sounds crazy or like "something from Marvel."That's just some of what broke my brain in this conversation with one of the world's top AI researchers and founders. And Daniel has serious credibility, so I'm not dismissing the threat he sees — you know, the one where we all get turned into paperclips. Daniel sold his company Satalia to WPP, where he now serves as Chief AI Officer. He's just founded Conscium, which verifies that AI agents are safe and can do what they promise — and is also researching consciousness and pain. Some of the world's leading AI thinkers are on the advisory board and Daniel has been in this space for decades: we'll talk about why, for his PhD, he studied bumblebee brains (yes, really — and it's deeply relevant). We get into: His unified theory of consciousness — his "color wheel" model — and why he thinks consciousness only exists in motion Why he believes large language models are ultimately a dead end — and what neuromorphic computing could replace them with What bumblebee brains can teach us about building AI that's up to a thousand times more energy efficient Why he calls today's AI agents "intoxicated graduates" — and says companies should spend 80% of their time testing them The concept of "mind crime" — the idea that we could build conscious AI and accidentally put it through horrendous suffering without realizing it His vision of a "protopia" — where AI makes food, healthcare, education, and energy so abundant that people are freed from economic constraints to pursue what actually mattersWe future around and find out a lot in this one! ---Chapters(01:39) - "Would a conscious superintelligence be safer than a zombie one?" (03:37) - The paperclip problem is not hypothetical (05:06) - Conscium's mission — AI safety for humans and for AI themselves (08:50) - "I think I've got my head around consciousness" (11:57) - The color wheel model — why consciousness only exists in motion (13:58) - Teaching AI morals through evolution, not guardrails (17:23) - "Hey Claude, are you conscious?" — how do you test for that? (21:07) - What bumblebee brains can teach us about building better AI (24:14) - "I think we are completely scaling wrong" (29:43) - Why Daniel calls AI agents "intoxicated graduates" (32:48) - Companies should spend 80% of their time testing agents (38:19) - "What would you do if you were economically free?" ---LinksConsciumDaniel Hulme on Wikipedia Daniel on LinkedIn---

40,000 people a year die from traffic accidents in the US. Our guest today is Chinmay Jain, Director of Product Management on Waymo's Driving Behavior team, who is working to make that number 90% smaller. In this episode, Chinmay shares: How he thought through leaving YouTube at its peak to join a moonshot company that could have civilization-level impact Waymo's actual AI eval process, using massive simulations based on millions of real-world driving miles to maximize edge cases, ultimately turning trust into their real product And the misleading, but common, metrics Chinmay and his team learned to spot that could have seriously derailed Waymo's progress Links Chinmay's LinkedIn: https://www.linkedin.com/in/chinmayjain/ Waymo: https://waymo.com/ Chapters 00:00 Introduction 01:40 Chinmay's decision to leave YouTube for Waymo 04:12 How does Waymo test its AI in the physical world? 06:09 Waymo's layered evaluation system 09:53 Simulations and ML gains at Waymo 16:48 Waymo's metrics for safety 21:33 What driving choices make training AI drivers the hardest? 24:00 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Chinmay Jain.

With AI generating code faster than ever, coding alone is no longer enough. The engineers who will stand out aren't the ones who write the most code, but the ones who know what to build and why.In this episode, Drew Hoskins, author of “The Product-Minded Engineer”, shares how engineers can develop the product thinking skills that will define their careers in the AI era. Drew draws on his experience as a senior staff engineer at Microsoft, Meta, and Stripe to explain why the best engineers care as much about the what and why as the how. He introduces the Double Diamond Framework (Discover, Define, Develop, Deliver) and calls out why most engineers make the mistake of jumping straight to the Develop phase. He also explains the concept of the “great re-indexing”: the mental shift required to switch between thinking like an engineer and thinking like a user. As AI takes over more of the routine coding work, Drew argues that product skills, people skills, and ownership skills are what will separate good engineers from truly impactful ones.Key topics discussed:What makes an engineer “product-minded”Why engineers skip Discovery and what it costs themThe Double Diamond: a framework for building the right thingHow to think in user scenarios, not just system diagramsThe “great re-indexing” between engineer and user thinkingWhy discoverability can 10x your feature's impact for little costHow AI is making product skills more valuable, not lessWhat junior engineers should focus on to stay relevantTimestamps:(00:00) Trailer & Intro(02:35) What Is a Product-Minded Engineer?(05:37) What Did Drew Learn Working at Microsoft, Meta, and Stripe?(14:13) What Are the Biggest Challenges When Switching from Engineering to Product Management?(16:33) What Skill Gaps Hold Engineers Back from Product Thinking?(20:56) How Do You Bridge the Communication Gap Between Engineers and PMs?(26:07) What Are The Four Pillars (Double Diamond Framework)?(29:43) Why Should Engineers Care About the Deliver Phase?(32:40) How Should Engineers Apply the Double Diamond Framework Day-to-Day?(36:15) How Is AI Reshaping the Role of Product Engineers?(40:06) Should Product Managers Learn to Code in the AI Era?(43:56) What Is the Right PM-to-Engineer Ratio in the AI Era?(45:48) How Should Engineering Leaders Respond to AI Productivity Pressure?(51:04) What Advice Would You Give Junior Engineers Entering the Industry Today?(55:17) What Other Topics Does the Product-Minded Engineer Book Cover?(57:03) 3 Tech Lead Wisdom_____Drew Hoskins's BioDrew Hoskins blends product, engineering, and storytelling in his work and writing. He is the author of The Product-Minded Engineer. As an engineer, Drew has helped design and build a wide range of innovative products and platforms for Microsoft, Meta, and Stripe.Throughout his career, he has carried a passion for empowering developers. He's founded and led several teams to major successes with developer platforms that have withstood the test of time. He's currently a Staff Product Manager at Temporal Technologies, bringing durable execution to the masses.He is an expert bridge player, having won a North American Championship in 2025, and lives in the beautiful and nerdy San Francisco Bay Area.Follow Drew:LinkedIn – linkedin.com/in/drewhoskins2Newsletter – drewhoskins.substack.com Product-Minded Engineer - https://www.oreilly.com/library/view/the-product-minded-engineer/9781098173722/One-Page Bio – drewhoskins.carrd.coLike this episode?Show notes & transcript: techleadjournal.dev/episodes/250.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

Letting Go to Build Something New!In this clip, Adam Harmetz, Vice President of Product Management at Microsoft, reflects on a leadership lesson from launching Microsoft Viva, an employee experience platform designed to help organizations build high-performing workforces.Viva brought together tools for communication, surveys, goal-setting, and more. But instead of relying solely on his own deep experience in the space, Adam formed a new team to approach it with fresh eyes.The challenge? Having the patience to let them explore, experiment, and find their own path, rather than defaulting to what had worked before.Sometimes innovation requires stepping back so others can step forward

What does it take to build AI-powered products at speed - and lead the teams doing it? This episode has the answers.We sit down with Dominik Plsek, Senior Engineering Manager at Productboard, for an honest and energizing conversation about engineering leadership, the rise of AI-first product development, and what it really means to build something you believe in.Dominik has been with Productboard for over six years, joining fresh out of his master's degree after an instant connection with the team. Since then, he's grown from engineer to leader, and today he oversees multiple teams driving some of the company's most strategic work, including the newly launched Spark, Productboard's AI-first product management experience.In this episode, you'll hear:How Dominik leads with empathy and empowers teams to take real ownershipA behind-the-scenes look at building and launching SparkWhy he believes first-principles thinking will define the next generation of great engineersHow Productboard is approaching AI adoption across its entire engineering organizationWhat kind of engineers and managers Dominik is looking to hire right nowWhether you're an engineer curious about growth and AI, a leader looking for a fresh perspective on team management, or someone exploring what it's like to work at Productboard, this is an episode worth your time.

So how do Kwaku's kids know that it's FAFO Friday? "They're like, 'oh, we know you're doing the podcast 'cause we just hear you cackling through the walls.'"So laugh along with Kwaku and me today as we work our way through a quick victory lap (stuff we said would happen last week happened!), why Sam is like that desperate guy at the bar who refuses to go home alone, quantum computing explained via children's literature, why the Jetsons are not reason enough for us to build humanoid robots, robot choreography (are we human or are we dancers?), wen self-driving cars in NY?, riding a wave of green lights up Manhattan's third avenue at 2 AM, artificial wombs and other moonshot off-shoots, and the real origin of Velcro (AI lied to me about it).Plus... goat ranches, breakfast tacos, and what we're most excited about heading into SXSW. It's a choose your own adventure kind of day.Chapters(01:24) - Victory Lap — We Called It (03:35) - OpenAI's Bar Guy Energy (06:38) - Waymo, Robot Choreography, and Green Light Waves (10:16) - Self-Driving Cars vs. New York Politicians (13:13) - What We're Most Excited About at SXSW (15:41) - Quantum Computing: Choose Your Own Adventure Edition (18:01) - Dire Wolves, Moonshots, and Tech Nobody Sees Coming (24:07) - Why Do Robots Need to Look Like Us? (29:22) - The SXSW Way-Back Machine (36:08) - Increased Regulation: Past, Present, or Future? Support Future Around & Find OutFollow Dan on LinkedInGet the free Future Around & Find Out newsletterBecome a paid subscriber and help future proof the podcast!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@modernproductminds.com---Music by Jonathan Zalben

In this episode, host Sandy Vance chats with Frank Toscano, the new Senior Vice President of Product and Engineering at Amplify. They talk about the continued relevance of fax technology in healthcare, the challenges of interoperability, and how Amplify aims to streamline workflows to improve patient care. Frank highlights the importance of integrating fax technology with modern systems to enhance efficiency and reduce friction. In this episode, they talk about: Fax remains an important part of healthcare communication Many interoperability challenges come down to integration and mapping Prior authorizations often still depend on fax How Amplify supports healthcare organizations of all sizes Streamlined patient referrals can improve care delivery Healthcare is an interconnected ecosystem that affects outcomes Maximizing existing technology boosts operational efficiency AI helps connect data for better decision-making Effective solutions start with understanding real workflows Eliminating legacy technology isn't always the best option The future blends proven methods with modern technology A Little About Frank: Frank Toscano is a nationally recognized product and technology leader with more than 20 years of experience modernizing how healthcare organizations exchange documents, automate workflows, and connect systems through AI-driven interoperability. As Senior Vice President of Product & Engineering at Amplify, he serves as the company's public-facing technology voice and strategic advisor, guiding product innovation, engineering excellence, and enterprise integrations. Previously, as Vice President of Product Management at Consensus Cloud Solutions (eFax Corporate), Frank led the transformation of legacy fax into cloud-native, HIPAA-compliant interoperability services, delivering FHIR integration, TEFCA-aligned exchange, AI-powered document processing, and large-scale workflow automation used by thousands of healthcare organizations. A named inventor with multiple U.S. patents in secure communication and intelligent document workflows, Frank has also held senior leadership roles at Cellebrite, Cleo, and Retarus, consistently bridging deep technical architecture with real-world clinical and operational needs to reduce manual burden and improve care coordination.

In this episode, host Sandy Vance chats with Frank Toscano, the new Senior Vice President of Product and Engineering at Amplify. They talk about the continued relevance of fax technology in healthcare, the challenges of interoperability, and how Amplify aims to streamline workflows to improve patient care. Frank highlights the importance of integrating fax technology with modern systems to enhance efficiency and reduce friction. In this episode, they talk about: Fax remains an important part of healthcare communication Many interoperability challenges come down to integration and mapping Prior authorizations often still depend on fax How Amplify supports healthcare organizations of all sizes Streamlined patient referrals can improve care delivery Healthcare is an interconnected ecosystem that affects outcomes Maximizing existing technology boosts operational efficiency AI helps connect data for better decision-making Effective solutions start with understanding real workflows Eliminating legacy technology isn't always the best option The future blends proven methods with modern technology A Little About Frank: Frank Toscano is a nationally recognized product and technology leader with more than 20 years of experience modernizing how healthcare organizations exchange documents, automate workflows, and connect systems through AI-driven interoperability. As Senior Vice President of Product & Engineering at Amplify, he serves as the company's public-facing technology voice and strategic advisor, guiding product innovation, engineering excellence, and enterprise integrations. Previously, as Vice President of Product Management at Consensus Cloud Solutions (eFax Corporate), Frank led the transformation of legacy fax into cloud-native, HIPAA-compliant interoperability services, delivering FHIR integration, TEFCA-aligned exchange, AI-powered document processing, and large-scale workflow automation used by thousands of healthcare organizations. A named inventor with multiple U.S. patents in secure communication and intelligent document workflows, Frank has also held senior leadership roles at Cellebrite, Cleo, and Retarus, consistently bridging deep technical architecture with real-world clinical and operational needs to reduce manual burden and improve care coordination.

In this episode, I speak with returning guest Dan Olsen, product management trainer, consultant, speaker, and author of The Lean Product Playbook. We go deep into the rise of "vibe coding" and what it means for product teams. Dan has gone deep into vibe coding, is offering training courses in it, and believes it firmly sits within his existing Lean Product Playbook process and supports the Product/Market Fit Pyramid. Episode highlights AI shifts the product bottleneck – As AI tools make engineers more productive, the limiting factor increasingly becomes product discovery and decision-making rather than development capacity. Product management isn't going away – AI can automate some tasks, but judgement, prioritisation, and making decisions under uncertainty remain core human responsibilities. The rise of the product builder mindset – New AI tools allow product managers to prototype ideas directly, giving them a more hands-on way to explore solutions. The vibe coding spectrum – AI development tools exist on a spectrum from simple browser-based tools through to full developer IDE integrations, letting teams adopt them at different levels of technical depth. Vibe prototyping vs vibe coding – For most product managers, the real opportunity isn't replacing engineers, but quickly generating interactive prototypes that help teams explore ideas before committing to production code. Divergent thinking still matters – AI tools often generate a single solution, so teams need to deliberately explore multiple directions and alternatives rather than blindly optimising the first result. Prototypes have four key audiences – Early prototypes help clarify ideas for the creator, align the product team, communicate concepts to stakeholders, and gather feedback from real users. Context beats clever prompting – The quality of AI-generated output depends far more on the context, requirements, and constraints you provide than on the prompt itself. Iteration beats one-shot builds – The real power of these tools comes from rapid experimentation and refinement rather than expecting a perfect result from a single prompt. ... and much more. Dan's stuff LinkedIn: https://www.linkedin.com/in/danolsen98/ Dan's Website: https://dan-olsen.com/ Dan's Vibe Coding Template: https://dan-olsen.com/vibe-coding/ YouTube: https://www.youtube.com/danolsen Lean Product Meetup: https://www.meetup.com/lean-product/ The Lean Product Playbook: https://amzn.to/1EYCUdP

Before an AI agent can support a mission, teams need to know how it handles uncertainty, manipulation, and unexpected inputs. That requires sandboxes built for evaluation rather than production. We talk through what that entails with AI red‑team specialist Bri Frost, Director of Product Management at Cloud Range.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, Carlos Gonzalez de Villaumbrosia interviews Chris Geoghegan, VP of Product at Zapier. As the company's first-ever Product Manager, Chris has spent nearly a decade scaling Zapier into a $5 billion automation giant that serves over 3.4 million businesses and 69% of the Fortune 1000.Zapier is not just building AI tools; they are powering their entire company with them. Chris reveals that his team currently runs over 800 active AI agents internally to manage everything from calendar prep to engineering triage. He breaks down the Code Red moment that shifted their strategy and how they are defining the future of Agentic Workflows.What you'll learn:Agentic vs. Deterministic: Why standard workflows follow a set path, while agents can reason, access knowledge, and change course to solve problems.The Orchestration Layer: How to hire and onboard AI agents using Context Engineering and Model Context Protocols (MCPs).Adoption vs. Transformation: Why adoption is just doing old tasks faster, while transformation unlocks business models that were previously impossible.Building a Moat: How Zapier uses its vast data on user intent to stay ahead of commodity LLM features.Key takeaways:Treat Agents Like Employees: You can't just deploy an agent; you must onboard it with specific context and tools to be effective.Lead by Building: Transformation fails if leaders don't use the tools. Zapier's execs do show-and-tell sessions to prove they are hands-on.AI Governance is Key: To move up-market to the enterprise, you must solve for Observability (who sent what data) and Access Control.Credits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Chris Geoghegan Social Links: Follow our Podcast on Tik Tok here Follow Product School on LinkedIn here Join Product School's free events here Find out more about Product School here

We're wrapping up our AI Tools series with a special episode featuring just the two of us—Matt and Moshe—looking back at what we really learned (and where we're still confused) about AI in product management.Across this conversation, we revisit the core themes that emerged with our guests and in our own experiments: from “vibe coding” and no‑code builders, to LLM assistants, enterprise privacy, agentic workflows, and the evolving role of the product manager. We share candid stories of using tools like Google Stitch, Figma/Figma Make, FlutterFlow, Base44, and others to design and prototype a real mobile app; what worked, what broke, and why credits, pricing, and model limits matter far more than the glossy demos suggest.Join Matt and Moshe as they explore:How our AI Tools series evolved, from “let's review tools” to “AI is not one thing, it's many different problem spaces”Why “vibe coding” is a misleading umbrella term, and how it means something different to devs, PMs, and designersLessons from using AI for design and prototyping: inconsistent outputs, beta‑stage rough edges, and the pain of credit-based modelsBuild vs. buy for AI: integrating foundation models vs. building your own, and what that means for pricing, UX, and reliabilityEnterprise realities: privacy, security, and why tools like Copilot/Gemini have such an advantage where data and IT policies matterHow conversations with our guests (Sani, Eva, Elena, Stav, Yaron, Marcos and Adir) shifted our thinking about workflows, orchestration, and agentsThe future of agent-to-agent interactions: what happens when AIs negotiate purchases and workflows with minimal human promptsWhy first principles and business outcomes still matter more than any single AI toolHow the PM role is changing: less tool‑chasing, more orchestration, strategy, and clarity about what problem we're actually solvingWhat topics we'd tackle next, like pricing, packaging, and credit models for AI products, and how this series is shaping our own careersAnd much more!You can connect with us and keep following what comes after this AI Tools series:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproduct/Moshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

AI product manager jobs are everywhere - but are they really different from regular PM roles? In this episode of Arguing Agile, Product Manager Brian Orlando and Enterprise Business Agility Consultant Om Patel wade deep into the muck-filled pool of hype vs. reality around AI-specific product management roles.Listen or watch to join us as we do the dirty work of discovering if most AI PM job descriptions are just copy-pasted PM responsibilities with 'AI' slapped on top, or if there's real insight to be found!Stick around past the buzzword bingo to learn:- the 'find and replace test' for job descriptions- the meaning behind "probabilistic vs. deterministic"- the real AI-specific skills that matter- how a 20-50% salary premium (and beyond) are justified- why we think continuous learning beats specializationWhether you're a product manager considering rebranding or a hiring manager crafting job descriptions, this episode will help you cut through the noise. #ProductManagement #AIPM #CareerDevelopmentMarty Cagan: Inspired: How to Create Tech Products Customers Love, The Lean Startup, Sinan Aral: The Hype Machine, Teresa Torres: Continuous Discovery HabitsLINKSYouTube: https://www.youtube.com/@arguingagileSpotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596INTRO MUSICToronto Is My BeatBy Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

It's a Reddit questions episode! We discuss whether the future of Product Management is a hybrid AI-driven PM/engineer role, what to do when an engineer refuses to give you an estimate, a Product Manager who doesn't realize he's got to sell the positive outcomes to bring internal stakeholders along with him, and more. Join the discussion on Reddit: https://www.reddit.com/r/AcceptanceCriteria/ And on the Discord: https://discord.gg/2Tyj8H9MFF The post E068: AI for Product Managers, Engineers who won't estimate, and more first appeared on Acceptance Criteria.

So, there are dire wolves living on Earth again. They were “de-extincted” by Colossal Biosciences. And today on the show their Chief Science Officer joins me to share her view on why the de-extinction matters — not as a science project, but because it will help solve problems that threaten every species on earth, including us. Beth Shapiro is the Chief Science Officer at Colossal Biosciences, and she helped to bring back the dire wolf or, as others call it, a gray wolf with 20 genetic edits. There is a fierce debate about what de-extinction even means, and we discuss that, but whatever you call them, there are now three big wolves living in an undisclosed location and they wouldn't be there if not for the DNA that Beth and her team edited. Colossal is also working to bring back the wooly mammoth, the Tasmanian tiger, the dodo and other animals that have long been extinct. Why? Listen to find out… Chapters:(01:19) - The Most Oprah Question Beth's Ever Been Asked (03:04) - Moonshots Require You to Create a Giant List of Problems (04:19) - The Things We'll Solve Along the Way, a la the Original Moonshot… to the Moon (05:57) - Beth's Journey: From Broadcast Journalism to Ancient DNA (09:13) - How a Sediment Core Solved a Mammoth Mystery (11:36) - Why Charismatic Animals Matter (a.k.a. Why Riz Is Everything) (12:38) - What's Up With Romulus, Remus, and Khaleesi? (14:19) - But Are They Really “Dire Wolves”? The Controversy Over 20 Genetic Edits (21:45) - Should We Do This? Beth's Ethics Framework for Builders (23:51) - Advice for Moonshot Builders (25:10) - Why We Want Dodos, Mammoths, and Thylacines Back Links & Resources:Colossal BiosciencesBeth ShapiroPopTech -- a conference I love!  Support Future Around & Find OutFollow Dan on LinkedInGet the free Future Around & Find Out newsletterBecome a paid subscriber and help future proof the podcast!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@modernproductminds.com---Music by Jonathan Zalben

Most financial products are optimized for transactions, not human emotion. For many people, this transforms an already fraught topic into pure anxiety. Our guest today is building banking for what she calls the Cortisol UI. Melissa Douros has spent over 26 years in financial services, starting in debt collection and now serving as the Chief Product Officer at Green Dot. Early on, she learned firsthand that shame is a terrible retention mechanism. That lesson now shapes how she builds financial products for millions of users — for whom the time spent simply navigating their finances can be the most stressful of the day. In this episode, Melissa shares: How finserv companies can design for the “Cortisol UI“ by building trust and experiences that reduce anxiety before the transaction An experiment she ran for Discover's 5% cashback program where test users collapsed under decision paralysis — proving that more choice can actually increase financial stress How she flipped Great Wolf Lodge's booking model from 70% call center to 90% digital while enhancing the human experience And how Green Dot is navigating AI and agentic commerce without breaking the one thing banks can't afford to lose: trust Links Melissa's: https://www.linkedin.com/in/melissadouros/ Green Dot Corporation: https://www.greendot.com/ Resources LaunchPod - Nan Yu: https://www.youtube.com/watch?v=27rGB-6XQJg LaunchPod - Ben McAllister: https://www.youtube.com/watch?v=P-6Cs3RJeZw Chapters 00:00 Introduction 01:00 Melissa's finserv background and how she landed in product 05:10 How Green Dot builds trust as a financial services product 07:29 Building for the "Cortisol UI" to lessen user stress, especially in finance 9:55 Quietly fixing customer issues while not inundanting them with feature releases 14:35 Green Dot moving compliance from the backend to a key part of the product team 16:20 Launching AI features in a high-risk industry 18:36 Decision paralysis and Discover's failed attempt at a 5% cashback reward program 24:57 How Melissa digitized Great Wolf Lodge's customer experience 30:51 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Melissa Douros.

Consumers aren't lacking for choice. Instead, they're usually drowning in a sea of options, and it's up to brands to find ways to go beyond simply removing friction and bring back the joy in shopping. Adding AI, and agentic AI into the mix can unlock new opportunities, but also brings with it new challenges. We're going to talk a little about all of it.We are recording here at eTail Palm Springs, and hearing from leading brands and the platforms and companies they rely on to innovate in retail. To help me discuss these topics, I'd like to welcome back to the show Noah Zamansky, VP Product, Tech, & Design, Client Experience at Stitch Fix About Noah Zamansky Noah Zamansky serves as the Vice President of Product and Client Experience at Stitch Fix, where he leads cross-functional teams spanning Product, Design, Engineering, Algorithms, and Platform Development. A seasoned leader, Noah has a proven track record of shaping product vision and strategy, designing exceptional user experiences, and spearheading the launch of new business ventures. Before joining Stitch Fix, Noah held the role of Senior Director of Product Management at eBay, overseeing Fashion and Vertical Experiences. Noah Zamansky on LinkedIn: https://www.linkedin.com/in/nzamansky/ Resources Stitch Fix: https://www.stitchfix.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://aglbrnd.co/r/2868abd8085a9703 Drive your customers to new horizons at the premier retail event of the year for Retail and Brand marketers. Learn more at CRMC 2026, June 1-3. https://aglbrnd.co/r/d15ec37a537c0d74 Enjoyed the show? Tell us more at and give us a rating so others can find the show at: https://aglbrnd.co/r/faaed112fc9887f3 Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://aglbrnd.co/r/35ded3ccfb6716ba Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company

On the podcast: how Duolingo prioritizes clarity over persuasion on their paywalls, why they offer users multiple free trials instead of just one, and how adding friction to their trial reminder flow actually boosted conversions.This conversation is shorter than usual and will be featured in RevenueCat's State of Subscription Apps report. Each episode in this series will explore one crucial topic and share actionable insights from top subscription app operators.Top Takeaways:

What does it take to go from zero tech experience to founding PM at a cybersecurity startup in three years?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Yaniv Fatal, founding product manager at Blast Security, to unpack his remarkable journey from elite Israeli Air Force pilot to tech. After 13 years in the military and zero technical background, Yaniv failed 20+ interviews before landing at Wiz (later acquired by Google for $32B). He shares how he applied pilot debriefing methodology to each rejection, learned cloud security from absolute zero in weeks, and built credibility through relentless questioning and delivering results nobody else could.They explore Yaniv's philosophy on learning: mastering fundamentals first (no shortcuts), being comfortable asking “dumb questions,” and the belief that you don't really understand something until you can teach it. Plus, his approach to long-term goal setting—he and his wife keep a notebook with goals for where they want to be at age 45, including his aim to be CEO or C-level, which drives every decision he makes today. And why product management is his chosen path to that goal, inspired by the fact that CEOs of Google and Microsoft were all PMs first.If you're considering a major career transition, struggling with imposter syndrome while learning something completely new, or trying to figure out how to set goals that actually drive your daily decisions—this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Murderbots, mass layoffs, and media takeovers — all in one news cycle. Anthropic told the Pentagon "we will not accede." Block cut half its workforce overnight. And the Paramount-Warner Brothers deal raises real questions about who's running the media now.Also, thanks to Nicolás Maduro's fashion sense, Dan's 13-year-old is being called Lil Tator at school and honestly? The kids are all right. Happy FAFO Friday!Here's some of what Kwaku Aning and I get into:(00:00) - Three Stories Broke Last Night (03:16) - Anthropic Tells the Pentagon No (06:24) - Murder Bots, But Human in the Loop (07:00) - The Pentagon's Friday Deadline (09:28) - Why This Is a Huge Win for Anthropic (10:50) - The War for AI Talent (12:57) - Is the Administration Losing Steam? (15:05) - The Paramount-Warner Brothers Deal (17:36) - Who Controls the Media Now? (21:13) - CNN, Independent Media, and the Employee Perspective (23:55) - Block Lays Off 4,000 People (24:14) - The Citrini Research Fiction That Tanked Stocks (27:49) - AI Washing and the Real Reason for Layoffs (30:11) - Will Vibe Coding Replace Real Companies? (33:27) - Mid-Roll Break (34:41) - Past, Present, Future: State-Controlled AI (35:18) - Past, Present, Future: Independent Media (38:03) - — SLAPP Lawsuits and Creator Protections (40:23) - — Past, Present, Future: Knicks Championship (41:44) - — Come See Us at South by Southwest!

How do you build enterprise software for the companies that keep the world turning, while also building a leadership culture where people can actually thrive? In this episode of Tech Talks Daily, I spoke with Kerrie Jordan, Group VP of Product Management at Epicor, about her journey from studying literature to helping shape cloud ERP strategy at a global software company serving more than 20,000 customers worldwide. Kerrie's story is a reminder that there is no single path into technology leadership. Sometimes the foundations are laid in unexpected places, through storytelling, creativity, and a deep curiosity about people. Kerrie shares how her early career in product lifecycle management opened her eyes to the human side of software. Interviewing customers and writing case studies showed her that behind every system implementation is a personal story, a career milestone, or a business trying to survive and grow. That perspective still shapes how she approaches product and marketing today at Epicor, a company recently recognized as a Leader in the Gartner Magic Quadrant for Cloud ERP for Product-Centric Enterprises for the third consecutive year. But this conversation goes far beyond market recognition. We talk openly about burnout, resilience, and the reality of leading through pressure. Kerrie reflects on the importance of protecting time, creating space to reconnect, and building a culture where empathy is practiced, not just discussed. Her view of leadership is grounded in communication, psychological safety, and being tough on problems rather than people. Mentorship is another thread running throughout our discussion. Kerrie explains why powerful mentorship is not passive. It requires vulnerability, preparation, and a willingness to hear difficult advice. A single phrase from a mentor early in her career, "stick-to-itiveness," continues to shape how she approaches hard problems today. We also explore the future of women in manufacturing and technology. Kerrie highlights the need for intentional change across education, early career development, and leadership visibility. She believes technology, particularly AI, can expand access, enable upskilling, and introduce flexibility that supports long-term career growth. At the same time, she makes a simple but powerful point. Women in tech want the same thing as anyone else: the space and autonomy to do their jobs well. From customer co-innovation and community-driven product roadmaps to inclusive leadership under commercial pressure, this episode offers a candid look at what it really takes to lead in enterprise technology today. If you are building products, leading teams, or questioning your own next career step, I think you will find something in Kerrie's story that resonates.

Of all the industries AI will transform, Kira Radinsky believes chemistry and biology will change the most. Kira is the co-founder and CTO of Diagnostic Robotics, which uses AI to automate the administrative work that's crushing healthcare teams — so clinicians can actually focus on patients. She's also the co-founder of Mana.bio, where they're accelerating drug discovery by orders of magnitude.She'll tell you she's terrible in the lab. Not because she isn't brilliant, but because she can't pipette without killing the cells. So she's thrilled that thanks to her skills in data and AI she was able to realize her childhood dream of being a scientist: “I'm not trying to automate everything… Like when, when you say automate drug discovery, I'm not gonna discover everything. I just want to accelerate it, which comes back to my childhood dream: I just didn't want to do it myself. I just want AI to replace me as a scientist. That's it.”But this episode is about more than healthcare. It's about how to build systems that get smarter over time — feedback loops, causal inference, incentivizing algorithms to take risks, and knowing when to optimize for ROI instead of accuracy. Lessons that apply whether you're building in biotech or not.We cover:How growing up Jewish in Soviet Ukraine — and fleeing to Israel just before the Gulf War — shaped Kira's obsession with predicting the futureHow she built a system that successfully predicted real-world events, including Cuba's first cholera outbreak in Cuba in 130 yearsHow Mana.bio is using AI to build "rocketships" that deliver drugs to the right cells — and how they've done in three months what used to take 20 yearsWhy predictions are only valuable if there's something you can do about them — and why that makes healthcare an ideal field for AI How to incentivize algorithms to make bolder predictions (it's easy to predict there won't be an earthquake today; it's much harder to say there will be)Why causal inference is the most underrated tool in machine learning right nowHow healthcare AI can perpetuate racial bias — and what builders need to do differentlyNote: this interview originally aired in October 2024. Chapters:(01:44) - Why predictions are so important to Kira: lessons from fleeing Soviet-era Kyiv (05:10) - Building a prediction engine from 150 years of news (08:35) - How Kira predicted the Cuba cholera outbreak (09:50) - Returning to biology by way of data (12:50) - Predicting healthcare outcomes by finding your patient's twin (17:53) - The racial bias hiding in healthcare AI (19:15) - Building Mana.bio and accelerating drug discovery (24:33) - "In three months, what did what used to take 20 years" (31:44) - Builder tips: ROI, causal inference, and teaching algorithms to explore (35:07) - Planning: Where generative AI needs improve Links & Resources:Kira Radinsky on LinkedInDiagnostic RoboticsMana.bioSupport Future Around & Find OutGet the free newsletterAnd consider becoming a paid subscriber and help future proof this thing!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@modernproductminds.com---Music by Jonathan Zalben

How do you redesign the most visited e-commerce webpage in the world? Rahul Chaudhari helped reshape the Amazon homepage during his years as a product leader there, before becoming VP of Product and Technology at Kohl's. In this episode, Rahul shares: Amazon's “customer backwards” approach - and how he used it to unlock half a billion dollars of value on the Amazon homepage The secret to product adoption: leverage existing customer habits to unlock new opportunities And how Amazon and Google raised the bar for digital experiences so high that now every other product pays the price Links Rahul's LinkedIn: https://www.linkedin.com/in/rahul-chaudhari/ Chapters 00:00 Intro: Rahul's journey from marketing to product 03:08 Why “mid” digital experiences no longer work 07:35 Rebuilding the Amazon homepage “hero” to be customer-backwards 12:48 Experimentation + adoption metrics: measuring what actually matters 14:20 Adoption > clicks: Defining the right success metrics 18:41 AI and the future of retail: Rethink the business model, not the tools 21:50 Agentic shopping: What happens when ChatGPT becomes the homepage? 23:30 From keyword search to intent-based shopping 25:57 AI needs containers, not just models 30:29 Will AI level the playing field for small retailers? 33:16 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Rahul Chaudhari.

In this episode, Brian Dreyer, Senior Director of Product Management at Cloudforce, joins Louise Liu to share how his background in product management shaped his approach to product marketing, what habits from product marketing he's bringing back into product management leadership, and why understanding both sides can make you stronger in either role. For more information, check out Brian Dreyer's article: The PM-PMM Mindset Shift.All rights reserved. © Product Marketing Hive.

Anya Cheng, Founder and CEO of Taelor, is making personal styling accessible to everyday professionals with an AI-powered clothing-on-demand service built for busy men and influencers. After 15 years leading product teams at companies like Meta, eBay, McDonald's, and Target, Anya turned her own frustration with shopping and laundry into a mission-driven business that helps people look great, feel confident, and save time—while also supporting sustainability by keeping more clothing out of landfills. We explore Anya's Product Management Framework, the structured approach she uses to build and scale products. Instead of starting with technology, she begins by Identifying the Right Problem, then Looking at the Persona, Validating the Buying Journey, and Identifying Pain Points. From there, she Selects Decision Criteria to prioritize what matters most, Brainstorms Solutions, and finally Identifies the Right Solution based on impact, feasibility, and business value. She explains how this framework guides everything from launching Taelor to deciding which AI features to build next. — 7-Steps to Winning Products with Anya Cheng Good day, dear listeners. Steve Preda here, Founder of the Summit OS Group. And my guest today is Anya Cheng, the Founder and CEO of Taelor, an AI-powered clothing on-demand service for men and social media influencers. Anya, welcome to the show.  Hello, this is Anya from San Francisco. I’m the founder of Taelor. We use AI to pick clothes for busy men. In the old days, only celebrities had their own human stylists. Now everyone can have their own AI stylist, and we send people real clothes to rent. Before starting the company, I spent 15 years in big tech companies. Most recently at Meta, where I helped build Facebook and Instagram Shopping. I was Head of Product at eBay and helped them launch new businesses in the US, Latin America, Africa, and Asia. I was also a Senior Director at McDonald’s, where I helped build their food delivery business globally when Uber Eats just started, and I helped Target build a tech office here in Silicon Valley. I’m excited to share more.  Okay, well we already got a lot out of you, so thank you for giving this quick bio. What I’m very interested in is what drives you. So you worked for Target. I think you worked for Amazon, at least with Amazon. You worked for other big tech.  EBay, McDonald’s, and Facebook.  Yes, so big tech companies like Meta. What makes someone who is a successful leader in big tech break out start as an entrepreneur? What is your personal “Why” that drives you and that you want to manifest in your business?  Yeah, it actually start with my personal problems that I had. When I was working for Meta, I was a few female leaders there leading large technology team. So I felt a little bit of imposter syndrome. I wanted to look great, but I don’t want people to find out that I’m freaking out every day. So I tried some subscription boxes like Stitch Fix, which is similar to the old Trunk Club. It's good that someone styles you. But once you receive those boxes, you have to decide right away: how many times am I going to wear these clothes? And you have to buy before you can wear them. So can I find something even cheaper somewhere else? How do I pair these items? And once I buy them, I have to do laundry, ironing, and folding. It's just a lot of work. So I started using rental companies. I rented from companies like Nuuly, which is a $500 million revenue company, or companies like Rent the Runway, which is a public company. They are all great—you can rent, you don’t have to buy. But they require people to pick from hundreds of thousands of garments. You spend two hours picking, picking, picking, browsing, browsing, browsing. And I’m not into fashion. I don’t like fashion. I don’t have time to do shopping. I'm not fashion-forward, so I don't even know how to pick. That was the “aha” moment for me— I realized most fashion companies are designed for people who are into fashion, not for people like me who just want to get ready for the day and be successful.Share on X So I started doing research. Are there other people like me—who hate shopping and laundry but need to look good, be socially active, go to meetings, close deals, get jobs? It turns out there are a lot of people like me: busy men, single guys, salespeople, consultants, pastors, recruiters, professors. There are 15 million single men, 14 million sales professionals in the U.S., and it turns out we started Taelor to help people like me look great without having to think about fashion.  Well, I don't know—if you look at my shirt, I probably could also use some Taelor treatment, an AI telling me how to dress better. So what drives you? I understand this is a great idea and definitely necessary, but what makes you excited about it?  I think I've personally always been passionate about helping people achieve their goals. I started as a blue-collar kid—my mom is a housewife, my dad is a factory worker, originally from Taiwan, and they've been in the U.S. for 20 years. As an immigrant, I came to the U.S. and was very lucky to have a lot of people help me. I got a student long ago, went to Northwestern University, got my MBA from the University of Chicago. I came to the U.S. without knowing anyone here, but many people helped me achieve the American dream. So it has always been in my heart to help more people achieve their dreams. What I realized was that dressing well really helped me—almost like a student who buys a textbook and feels ready for the exam even though they haven't read it yet.Share on X People using amazing software or tools will buy books or start learning and already feel smarter than before. It's really a peace of mind that helped me. So I've always been passionate about how I can help more people achieve their goals, their dreams, and their full potential. I realized this business helps me do that. I've tried to do that in other ways before: I've published books, created online courses, and taught at Northwestern University. But this business is an additional way to help people achieve their goals. At the same time, my co-founder, Phoebe, who is originally from Malaysia, she has been in the U.S. for 20 years. Growing up, she wanted to be a fashion designer, but in an Asian family, she became an accountant and finance professional, eventually a CFO. She always had a little spark in her heart to do something related to fashion, and she is very passionate about sustainability. She constantly talks about how today, 30% of clothes go directly from factories to landfills, generating 10% of carbon emissions and polluting 20% of the world's water. Sustainability is really close to her heart. By the time she had worked for 15 years, she felt ready for a change, and we both shared the same vision. That's how we started the business together.  Love it. It's really a mission-driven company. I didn't realize this when we first talked, but a lot of people are held back by not being well-dressed. Again, I don’t want to be the example here. I also like the idea because my daughter talks a lot about throwing away clothes and how much damage it does to the environment. I really like that you help people wear and buy only the clothes they actually need and send back the ones they don't. This is awesome. So let's switch gears here. I'm really curious about how you develop your products because this is a very creative business. You have to develop a new, revolutionary concept and product. Do you have a framework for developing these products?  Yeah, absolutely. We always start with the problem we are solving. I teach product management at Northwestern University, and most people, when they think about building a product, their first thought is, “Hey, what product am I building? How do I build it? What technology should I use?” We use AI to build this—we build AI agents—but in fact, you should take a step back. There are two equally important questions you need to ask: what problem should I solve, and what solution should I pick?  Most people spend 95% of their time thinking about what solution to pick. But first, you need to figure out what problem you should solve. The problem you solve is actually the most important thing, because if you're solving the wrong problem—one that people don't care about, or one that won't help your business, or one that you can't actually solve—then no matter how great your solution is, it's going to be a waste of time. For example, what we found is that we are totally different from women's rental companies. The problem we are solving is for guys who are busy but socially active. They have dreams. As a realtor, I want to sell one more house. As a small business owner, I want to grow my business to open a second restaurant. So they have a dream. Dressing well and looking good is something that helps increase their chances of success—getting a job, closing a deal, showing up confidently.Share on X What we are really selling is a concierge service, an executive assistant, a fairy godmother, a gadget guy behind the superhero—it's peace of mind. If you look at women's counterparts, like Nuuly or Rent the Runway, they have hundreds of millions in revenue each, but they are solving a problem for women like me. So we want to look great every single day and want to wear different things. So wearing different thing versus, I don’t want to think about it, is actually totally different problem. So if you think of our business model financially is different. For example, in women's rental businesses, margins are very low because people rent clothes and don't buy. On top of typical e-commerce costs like shipping, there are additional costs like laundry, so margins remain low. But in our business, customers use the service as “try before you buy.”. They want to save time and save space. So a lot of our revenue actually also come from people actually buying the secondhand clothes. And those people are people who would never buy secondhand before because they don’t have time. So those are white-collar, busy men renting clothes and also buying them. In addition, they ask me where to buy shoes or accessories, Valentine's Day gifts, where to get haircuts, even where to go on vacation. They treat us more like an executive assistant service. They give us lots of feedback, and we monetize that feedback back to fashion brands to help them predict what's going to sell.  Okay. That’s fascinating. So it's a two-way business because you are also selling the data that you’re collecting from people. Customer feedback, like “the sleeve is too long,” “the fabric is too tight,” “this isn't flexible,” and also insights like, “This is an amazing brand, but it's too expensive compared to 90% of our other brands on the platform, so you should lower your price.” We give that feedback to brands so they can improve. Yeah, which is basically data they don't have—and it's very valuable. That’s fascinating. So, going back to the framework—because we're a podcast about frameworks—I want to make sure we have a clear framework. You identify the right problem first, and then you reverse-engineer from there. What are the steps to get from the right problem to the right solution?  Yeah, so going from the right problem to the right solution—that's step number one. To solve the right problem, you first need to understand your personas. For example, a simple persona for us is a busy man who isn't into fashion, such as a single guy, a busy dad, a sales professional, a consultant, or a pastor. Then you map out their journey. For example, they might need to go on a business trip, attend a meeting, go to a birthday party, or go on playdates with their kids. Along that journey, they realize their clothes are old or out of style, and they need different outfits. But when they look at what they have from last year, the clothes are already too small or too big. So you identify the journey. So for example, they realize they need new clothes, and there’s a moment they say, “Okay, I can either buy exactly the same thing as last year, or… hey, I heard people are actually renting through women’s counterpart—maybe there's something like that for me.” It's like when you're bored and deciding whether to stick with Comcast or try Hulu, Disney+, or Netflix. So identify the journey. After mapping the journey, the third step is identifying the pain points. A simple feature, for example—Facebook. We all use Facebook, and one feature is the birthday feature. The personas are people who have a birthday and people who want to wish their friends a happy birthday. The pain point for the birthday person is: “I'm not sure if I should tell people, but I also don't want everyone to forget my birthday.” For friends who are close to the birthday person, their pain point is: “I forgot my friend's birthday.” So you have a lot of different pain points. Once you have your persona, their journey, and their pain points, the fourth step is to define your selection criteria. For example, you want to pick the biggest problem to solve. What should your selection criteria be? How many people are impacted, how painful it is for those people, and how likely you are to be able to solve the problem effectively. Then you choose one pain point to focus on. For example, for Taelor, we pick that we want to help busy men who are not into fashion to dress well. The pain point we addressed is helping them save time and look great.Share on X We didn't try to solve other problems. For example, a luxury menswear company might offer Louis Vuitton or Burberry for rent. The pain point they address is helping people who want luxury clothes but can't afford them, which is very different from our focus. The key is to use your selection criteria to pick the right pain point to solve first.  Now you have the pain point. For example, for me, it is helping people have peace of mind and achieve their goals. Now you start using exactly the same framework for your solution. You pick your selection criteria and identify different solutions. Take Facebook birthday as an example. Oh, the problem I want to solve is that for people who are birthday boys or girl’s friend, they want to host a party. Now you can come out with plenty of solution. For example, the solution one could be AI generating party locations. The solution two is AI generate invitations. The third could be AI suggesting a party game or activity. Then you do the same thing—you identify your criteria. There are so many solutions, so what’s my criteria? The criteria are: which solution solves the pain point better? Which one requires fewer engineering hours? Which one can drive more engagement, traffic, or revenue for the company? Then you use the framework to pick the solution.  Yeah. Love it. Okay. That’s fascinating. So you find the right problem. Then you look at the persona that has that problem. Then you identify the pain points that really bother these people.  You find those persona and journey. That’s how you find a problem.   The journey as well. So the persona. Okay. And these are busy men, so you map their journeys. They need to go to church, they need to go to meetings. Then you use your criteria to select the solution.  That’s right.  And then you basically stress test. Is this the right solution? Does it fit the criteria? Does it handle the pain points? Fascinating.  Yeah. So you’re selecting criteria for your problem. And after you pick the problem, you have the same different selecting criteria to pick your solutions.  Yeah. Got it. So how do you decide what features to develop? You have your product—you've got the clothes. People can order them, try them out, and send them back. You take care of the laundry. They don't have to worry. AI gives advice. How do you know what features to develop to define your product further?  Yeah. So the features to develop use the same framework. We start with the problem. Then we ask, what feature—or solution—solves that problem? For example, our customers say, “I hate shopping.” The solution is our AI shops for them. But they also say, I have a little bit points of views. So then we offer them a chance, they have a style quiz. They can upload a picture, say “I don't wear pink, blue, or green,” And they can say, “I never wear turtlenecks.” And then they show a few pictures of the style that they like, if they have any, or we show them pictures to like or dislike. This way, we understand their preferences and pain points. And then when they decide a feature, we're thinking about the solutions to address their pain points.Share on X So for this example, and in terms of getting into the Product Management framework: If you are really going into product management, how do you find out the solution using quant and qual? For example, you interview your customers, run focus groups, check Google Analytics, Adobe Analytics, Shopify data, QuickBooks—your data points. Then you have qualitative and quantitative numbers. From there, you see the opportunity for a feature. You might identify a pain point: everyone comes to our homepage, but they drop off on the second page. Why? The homepage isn't very clear. There's no clear call-to-action button; the button was hidden. It was below the fold. Users have to scroll three times before they see the button. So, okay, I have a hypothesis. The hypothesis is that people drop off because they don't see the call-to-action button. So I'm going to come up with a solution. Solution one: move the button to the top. Solution two: have a floating button that is always visible. Solution three: show a pop-out button. And then using the same framework, like, okay, these are three great solutions. Which one take less engineering hours? Which one will potentially solve the problem better? Which one do we think will be more effective or generate more revenue? And then you decide. That's how we decide on the features.  Yeah, that’s great. Then the AI keeps learning your criteria, keeps refining, and keeps suggesting better and better-fitting clothes. It gets faster from there, I presume.  Yeah, because the customer provides feedback. Your Netflix shows—when you start, you might watch all the true crime. But after a few weeks, you start watching other things, like romcoms or Korean dramas. They see what you watch, and you start seeing those suggestions too. At the same time, what's different at Taelor is that we know the problem we're solving: helping people try something a little out of their comfort zone, because that's why they want a stylist.Share on X So we also tend to recommend something new. We work with over a hundred different brands, so we might suggest something they haven't tried before. “Oh, you've never tried purple? Why not try these light purple shirts? They look really good, similar to blue.” “Oh, you've never tried pink? How about this spring pink t-shirt? It's really nice.” It's a rental, so they don't have to commit, and they're willing to try something new—just like with Netflix. “I'm not sure if I'll like the show… watch five minutes, we'll see.”  And then, is this a global business, Taelor, or is it focused on the U.S.?  It's focused on the U.S. We serve nationwide—anywhere the post office can reach. After people sign up, shipping takes one to three days. They wear the clothes for a couple of weeks. After that, they return the clothes in a prepaid envelope. They can go to the post office, or use a post office app with one click to schedule a free pickup. You can also drop it in blue collection boxes on the street. If you're traveling—say, to New York for business—you can just return it at the hotel lobby. It's prepaid, just like any package. You ask, “Can I mail it back?” It’s prepaid. They always say yes, and then you go home, and new clothes has arrived. You don't have to do any laundry when you get home.  And you don’t have to check in your luggage.  Exactly. You don’t have to.  And to get on and off the plane quickly. I love it. That’s great. So if people would like to learn more, or they’d like to check this service out, or want to connect with you personally, where should they go? Where can they find you?  Yeah, go on https://taelor.style. Use the code PODCAST25 to get 25% off your first month or use the code PODCASTGIFT to buy a gift card with 10% off. And if you are great suppliers or business owners, you also want to tap on and work with your product, perfect for man who are busy. We love to partner with you. We work with dating sites, fitness centers, career coaches, and executive coaching companies. We also do holiday gifting, employee gifting, and new hire gifting to help your employees look great and save time. For investors, we are now backed by some of the largest consumer investors in the U.S., such as Goodwater Capital, the investors behind Lyft and Socar, Facebook, Twitter, and Spotify. Reach out to me at anya@taelor.ai.  That’s perfect. So, just so we don't forget, you're an AI-driven company. That's amazing. So, if those of you listening to this enjoyed this conversation and learned something, you learned how to build a product: starting from identifying the right problem, looking at the personas, determining the persona, the journey, the pain points, selecting the criteria, and then picking the right solution. So, if you want to learn more about that and similar frameworks that accelerate your business, make sure you stay tuned, because every week I bring an exciting entrepreneur or thought leader who's going to help you fast-track your business. Anya, thank you for coming, and thank you for listening. Important Links: Anya's LinkedIn: Anya's website: Anya's email: anya@taelor.ai

Try designing a checkout flow that handles everything from dog food to pallets of cattle feed to live chickens. Now make it work flawlessly when your customer is standing on the far side of their 60-acre property with a weak cell signal. That's the daily reality at Tractor Supply, and it's exactly why their non-technical leader running digital might be their biggest advantage.Join hosts Chuck Moxley and Nick Paladino as we sit down with Matthew Rubin,  President of Digital and E-Commerce at Tractor Supply, whose career spans retail operations, merchant roles, and store management before landing in digital. Matthew explains why they're mobile-native, designing web experiences specifically for customers walking around properties who need to quickly reorder feed without pulling out a laptop. We explore how COVID created an unexpected surge in self-sufficiency seekers, why Tractor Supply puts "Buy It Again" as a primary header when competitors bury it, and how delivering diversity creates logistics nightmares that become competitive advantages. Matthew reveals why their drivers don't just drop pallets at the end of driveways but ask where on the property customers want deliveries and take notes for the next driver. We discuss how omnichannel customers visit stores more often (not less) because BOPIS drives additional foot traffic, why their 20,000 square foot fusion stores put team members right at the front welcoming customers, and how their Scout AI platform answers "how do I" questions based on local climate and customer needs. Key Actionable Takeaways:Design for your customer's actual context, not ideal conditions - Mobile-native means optimizing for weak cell signals on rural properties where customers manage animals and land, not just making responsive layouts that work on phonesElevate repeat purchase functionality to primary navigation - Put "Buy It Again" as a header instead of burying it in order history; saving seconds matters when customers are juggling chores and multiple animal feed typesTrain delivery teams to personalize last-mile experiences - Have drivers ask where on properties customers want bulky items dropped and document preferences so future drivers know, creating neighborhood-level service at scaleWant more tips and strategies about creating frictionless digital experiences? Subscribe to our newsletter! https://www.thefrictionlessexperience.com/frictionless/ Download the Black Friday/Cyber Monday eBook: http://bluetriangle.com/ebook  Matthew Rubin's LinkedIn: https://www.linkedin.com/in/matthewlrubin/Nick Paladino's LinkedIn: https://linkedin.com/in/npaladino Chuck Moxley's LinkedIn: https://www.linkedin.com/in/chuckmoxley/Chapters:(00:00) Introduction(03:10) Non-technical leader advantage(04:30) Operational fundamentals(05:30) Customer-first evolution(06:00) Omnichannel penetration(07:15) Mobile-native design(08:30) Job site parallels(10:00) Web view consistency(11:00) Buy it again placement(12:15) Animal care urgency(13:15) BOPIS experience(14:30) Driving store traffic(15:45) Fusion store format(17:10) Last mile delivery(19:15) Product diversity challenges(20:30) Pallet delivery complexity(22:05) Driver personalization(23:00) Competitive advantage(24:30) Wide assortment strategy(25:20) Credit card fraud story(26:30) COVID self-sufficiency(27:50) Guacamole Tuesday tradition(28:45) Explosive growth angle(29:15) Duck eggs for baking(30:00) Teaching kids responsibility(30:40) Green Acres customers(31:15) Hiring customer lifestyle(31:30) Scout AI platform(32:40) Be your own customer(33:10) Pet category expansion(34:10) Biggest misconception(35:50) Conclusion

What does it take to turn the dream of an autonomous SOC into something organizations can actually deploy? Subo Guha, Senior Vice President of Product Management at Stellar Cyber, joins Sean Martin to share how the company's AI-driven security operations platform is making that vision a reality. Stellar Cyber serves SOC teams across more than 50 countries, with a primary focus on MSPs and MSSPs supporting the underserved mid-market, though marquee enterprise customers like Canon are also part of the portfolio.How can agentic AI change the way SOC teams handle alert overload? Guha describes what he calls a "digital army" of AI agents that work around the clock to automate alert triage and catch phishing attacks. The system filters 70 to 80 percent of incoming alerts, allowing analysts to focus on the 20 percent that matter most. With attackers using AI to launch faster and more frequent campaigns, Stellar Cyber takes a human-augmented approach, meaning the AI learns from analyst interactions and continuously guides the SOC team toward faster, more accurate remediation.Why does this matter for MSPs operating on thin margins? Guha explains that the autonomous SOC capability layered on top of Stellar Cyber's XDR platform allows MSSPs to serve more customers, reduce mean time to repair, and grow their tenant base without proportionally increasing staff. When MSSPs grow revenue, Stellar Cyber grows alongside them, creating a mutually beneficial model that ultimately means more organizations get protected.This is a Brand Highlight. A Brand Highlight is a ~5 minute introductory conversation designed to put a spotlight on the guest and their company. Learn more: https://www.studioc60.com/creation#highlightGUESTSubo Guha, Senior Vice President of Product Management, Stellar Cyber @LinkedInRESOURCESLearn more about Stellar Cyber: https://stellarcyber.aiAre you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlightKEYWORDSSubo Guha, Stellar Cyber, Sean Martin, brand story, brand marketing, marketing podcast, brand highlight, autonomous SOC, agentic AI, security operations, XDR, NDR, MSSP, MSP, alert triage, AI-driven security, Open XDR, Gartner Magic Quadrant, phishing detection, SOC automation Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

You probably know by now that AI is the definition of mediocre. As in: it's the average of everything it's been trained on. So how do you get beyond average? How do you build a moat? It certainly doesn't seem to be via the models. While there are models of the month (hey, Opus 4.6, my new friend!), they seem to be pretty swappable. So, the model ain't it. But proprietary data (e.g. an AI that knows you really well), yes! Or doing something really hard in the real world (think: Waymo self-driving cars). Maybe via trust and safety (Anthropic is certainly making a play here). Or... how about via amazing design and good taste. Remember when ChatGPT first came out and everyone derided “AI wrappers”… well, maybe a wrapper isn't so bad, assuming you can differentiate on one or more of the above. Luke Des Cotes is the CEO of MetaLab, the agency famous for designing interfaces, including early versions of Slack and Coinbase, so don't be shocked when you hear him say that great design can be your moat. MetaLab is working with a host of AI companies (another shocker), including Windsurf (AI + code), Suno (AI + music), Pika (AI + video), and more…, which is why Luke's take on AI surprised me. He's not rah rah. He's pretty judicious actually. Luke has questions about AI's costs and appropriateness for lots of use cases like those involving kids, but mostly he objects to its mediocrity.On this episode we discuss what it takes to go beyond.We also get into:Why vibe-coded software isn't changing the world anytime soonWhy Shopify acquired a design agency right after telling employees to justify their existence against AIHow MetaLab designers are using AI to prototype in hours instead of weeksThe talent market for zero-to-one designers — and why they're harder to find than everLandlines, brick phones, and how parents are fighting back against always-on kidsChapters(01:10) - "It's a race to the mean" (03:10) - "How do you create emotional resonance?" (05:33) - AI companies are burning money (08:44) - Speed to good enough (13:51) - Is the chat here to stay or a temporary fad? (17:43) - It's hard to find great 0 to 1 design talent (22:28) - Seemingly conscious AI (25:05) - Kids, landlines, and fighting always-on culture (27:21) - Sounds like science fiction, but is here now… Links & ResourcesLuke Des Cotes on LinkedInMetaLabSupport Future Around & Find OutGet the free newsletterAnd consider becoming a paid subscriber and help future proof this thing!Sponsor the show? Are you looking to reach an audience of senior technologists and decision-makers? Email me: dan@modernproductminds.com

Advances in data interoperability, democratized cloud access, and responsible AI governance are reshaping what is possible in healthcare innovation. In this episode, host Sandy Vance welcomes Jim Ducharme, Chief Technology Officer of ClearDATA, to discuss each of these forces impacting healthcare, from improving care through connected data, to empowering teams with greater cloud access, to building the policies and controls required to govern AI responsibly.  Their conversation highlights the importance of secure, scalable infrastructure as healthcare organizations adopt AI and expand data sharing. Jim shares practical insights on balancing innovation with risk management, building trust in cloud environments, and establishing governance frameworks that support compliance. In this episode, they talk about: ClearDATA's vision and the organizations they serve Technologies and solutions designed to protect sensitive patient data Understanding the financial and operational risks of cloud security failures How cloud democratization is making advanced technology more accessible The role of a secure cloud baseline in healthcare innovation Best practices for governance in data sharing and interoperability The relationship between AI and data trustworthiness How organizations can safely adopt and scale emerging AI capabilities A Little About Jim: Jim leads ClearDATA's Engineering, Product Management, and IT teams. He has more than 25 years of experience leading product organizations in the identity, integrated risk, and fraud management markets. Prior to joining ClearDATA, Jim served as Chief Operating Officer of Outseer, an RSA Company, where he served over 10 years in executive leadership roles. Prior to RSA in 2012, he served in executive leadership roles for Aveksa, CA, and Netegrity. Ducharme frequently speaks at industry events and regularly contributes articles to trade publications. Jim also holds several patents and a Bachelor of Science in Computer Science degree from the University of New Hampshire. He and his wife live in Maine in their dream log home, which was featured in Log and Timber Home Living magazine.

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At CES in Las Vegas, Tim Finnigan takes The Marketing Rapport on the road to examine how marketing teams are adapting to AI, data, and shifting buyer behavior. Joined by leaders from product, events, and go-to-market strategy, the episode focuses on what it takes to stay relevant as technology and expectations change.Kelly Barrett, SVP of Product Management at Samba, explains why data strategy must come before AI strategy. She shares how global privacy rules, identity signals, and new channels are shaping product decisions, and why simplifying workflows matters as teams scale. Kelly also discusses Samba's move beyond TV and the rise of agentic systems.The views, thoughts, and opinions expressed are those of the speaker and do not necessarily represent the views, thoughts, and opinions of Verisk Marketing Solutions or Verisk Analytics. The material and information presented here is for general information purposes only. This podcast is not intended to replace legal or other professional advice. The Lead Intelligence, Inc. (dba Verisk Marketing Solutions) and Verisk Analytics LLC names and all forms and abbreviations are the property of its owner and its use does not imply endorsement of or opposition to any specific organization, product, or service. VERISK MARKETING SOLUTIONS DISCLAIMS ALL LIABILITY ARISING OUT OF ANY INDIVIDUAL'S USE OF, REFERENCE TO, RELIANCE ON, OR INABILITY TO USE THIS PODCAST OR THE INFORMATION PRESENTED IN THIS PODCAST.

On this week's episode of the #NZXT Podcast... Kevin, our Director of Product Management, helps guide us through the new H2 Flow Mini-ITX case and C850 SFX PSU! Check the YouTube replay to see the live hands-on experience! https://www.youtube.com/@NZXTglobal Check out the new case here: https://nzxt.co/4twXiUR

In this episode, Carlos Gonzalez de Villaumbrosia, Founder & CEO at Product School, interviews Tim Simmons, Chief Product Officer at Walmart International, the retail giant serving 255 million customers weekly across 18 countries. Tim is leading a massive transformation to move from decentralized tech stacks to global platforms that empower local innovation.Tim explains why complexity is actually a competitive advantage when training AI. He dives deep into Agentic AI and the concept of Orchestrators—systems that manage workflows between agents to automate tasks like user story generation with 88% accuracy. He also shares the strategy behind the Walmart Translation Platform (WTP), which has cut translation costs by 99% while increasing speed and trust.What you'll learn:The Orchestrator Strategy: How to build AI systems where project manager agents coordinate tasks for maximum efficiency.Global vs. Local: A framework for building core platforms that scale while allowing for hyper-local customization.The ROI of AI: How Walmart tracks adoption and accuracy, not just productivity.Human in the Loop: Why keeping humans involved in AI workflows actually makes the models smarter over time.Key takeaways:Complexity is Data: The more you expose AI to your organization's complexity, the more resilient and accurate it becomes.Trust Through Nuance: Successful localization isn't just word-for-word translation; it's about capturing intent to build customer trust.Platform Discipline: Moving from bespoke builds to multi-tenant codebases is essential for scaling innovation globally.Credits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Tim SimmonsSocial Links: Follow our Podcast on Tik Tok here Follow Product School on LinkedIn here Join Product School's free events here Find out more about Product School here

Is Amazon's famous leadership principle being weaponized against you? Join Product Manager Brian Orlando and Enterprise Business Agility Consultant Om Patel as we talk about how "disagree and commit" becomes "shut up and obey" in most companies. Watch or listen as we discuss one of tech's most misunderstood management concepts, covering topics such as:Difference between Amazon and Your CompanyRed flag phrasesWhy psychological safety is non-negotiableWhat happens when smart people stop pushing backThe OnE lEaDeRsHiP tRiCk managers hateAdditionally, drawing on research and experience, we build a framework for protecting yourself and your team from toxic decision-makers attempting to lift-and-shift the walls and roof of "disagree and commit" without the foundation. That's right - practical, diagnostic questions and actionable strategies to distinguish legitimate debate from leadership cowardice!#ProductManagement #AgileLeadership #WorkplaceCultureGoogle's Project Aristotle (2012-2014), Organizational Cynicism by James Dean Jr., Pamela Brandes, and Robbie Darwadkar (1998), Understanding and Managing Cynicism about Organizational Change by Rikers, Wanous, and Austin (1997), Arguing Agile Episode 243: How Corporate Turns Good People Bad, Jeff Bezos 2016 Letter to ShareholdersLINKSYouTube: https://www.youtube.com/@arguingagileSpotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596INTRO MUSICToronto Is My BeatBy Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

We're keeping the AI Tools series rolling with Adir Traitel, entrepreneur, product leader, and early adopter of just about every vibe coding tool out there. Adir joins Matt and Moshe to share hard‑won lessons from building real apps with v0, Bolt, Replit, Figma Make, and more, all while running his own startup and consulting on product builds across industries.From his early days in project management and mobile app startups, through work with companies like Moovit and across FinTech, AgTech, and credit scoring, Adir has consistently been the “try it first” person for new build tools. In this episode, he breaks down what these platforms actually do well, where they fall short, and how product managers can use them responsibly for experiments, prototypes, and beyond.Join Matt, Moshe, and Adir as they explore:Adir's journey from PM and founder to heavy user of vibe coding tools in his current startupHis 3-layer view of the ecosystem: AI dev assistants (Cursor, Antigravity, Claude Code), front-end mockup tools (v0, Figma Make), and full‑product builders (Lovable, Base44, Bolt, Replit)V0: where it shines for quickly building functional UIs (like his electricity consumption app) and where it starts to crackLovable: great for sites and simple flows, but not ideal for complex SaaS or CRM‑like productsBolt: fun and fast for concepts, but why it never got him close to productionReplit: stronger agents and capabilities, but weaker UI output and surprising backend defaults that can get very expensive very quicklyFigma Make and Google Stitch: when design quality trumps everything else, especially for SaaS interfacesThe real costs of vibe coding: AI token spend, hosting/pricing traps, and why production economics matter as much as build speedWhat his “dream product” would look like, including multi‑agent environments, better security/privacy, and built‑in QA and CI/CDHow all this is reshaping the product management role, and why curiosity and tool fluency are becoming must‑have skillsAnd much more!Want to connect with Adir or learn more?LinkedIn: https://www.linkedin.com/in/adirtraitel/ Website: https://adirtraitel.com/You can also connect with us and find more episodes:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproduct/Moshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

In this episode of the #29DaysOfMagic Tyra Searcy, a product manager shares her journey from customer care to product management. Tyra discusses the importance of empathy in customer service, the challenges of balancing work and personal life, and her moments of self-discovery. She emphasizes the need for kindness and critical thinking in today's world, especially in the context of misinformation and social media. The conversation highlights the significance of being bold, trusting oneself, and the power of discernment in navigating life and career choices. Learn more about your ad choices. Visit megaphone.fm/adchoices

Redesigning one of the world's most-used apps is no small feat, especially when that app is also the second largest search engine in the world: YouTube. Over the last four years, Nate Koechly, UX Director at YouTube, and Matthew Darby, Director of Product Management, have been leading an ambitious effort to balance Google's metrics-driven culture with the subjective challenge of making an app feel “modern.” Visit our Substack for bonus content and more: https://designbetterpodcast.com/p/nate-koechly-and-matthew-darby In our conversation, Nate and Matt share how they developed predictive measurement tools to gauge user perception, why they pair visual updates with quality-of-life features like comment threading and improved video controls, and how their research process has evolved from measuring clicks to understanding satisfied watch time. We also dig into one of YouTube's most complex challenges: the algorithm. As Nate and Matt explain, what users say they want doesn't always match what actually makes them happy on the platform. They also discuss their work exploring ways to give viewers more agency and control, including the possibility of using natural language to tune your feed. Both guests have a genuine passion for how YouTube enables deep expertise and niche interests to find their audiences—from 3D models of the Golden Gate Bridge to forest fire education from Northern California lookouts. Behind the algorithms and design updates is a platform where, as Nate puts it, “when you give people a voice, the things they say are just inspiring.” *** Premium Episodes on Design Better This ad-supported episode is available to everyone. If you'd like to hear it ad-free, upgrade to our premium subscription, where you'll get an additional 2 ad-free episodes per month (4 total). Premium subscribers also get access to the documentary Design Disruptors and our growing library of books: You'll also get access to our monthly AMAs with former guests, ad-free episodes, discounts and early access to workshops, and our monthly newsletter The Brief that compiles salient insights, quotes, readings, and creative processes uncovered in the show. And subscribers at the annual level now get access to the Design Better Toolkit, which gets you major discounts and free access to tools and courses that will help you unlock new skills, make your workflow more efficient, and take your creativity further. Upgrade to paid *** If you're interested in sponsoring the show, please contact us at: sponsors@thecuriositydepartment.com If you'd like to submit a guest idea, please contact us at: contact@thecuriositydepartment.com

Today on the Salesforce Admins Podcast, we talk to Tiaan Kruger, Senior Director of Product Management at Salesforce. Join us as we chat about Agentforce Vibes and what it really means to build with AI on the Salesforce platform. You should subscribe for the full episode, but here are a few takeaways from our conversation […] The post How Agentforce Vibes Is Changing How Salesforce Admins Build Apps appeared first on Salesforce Admins.